This application requests continued support for the long-term objectives of AA 011034, namely to develop and implement high throughput strategies for investigating the natural genetic variation in alcohol- related behavioral phenotypes. Historically AA 011034 has focused on the detection and fine mapping of quantitative trait loci (QTL) and on the integration of QTL mapping with gene expression and sequence data. This competing renewal continues this work with a focus on 7 coincident-reciprocal QTLs detected for acute ethanol withdrawal and ethanol consumption/preference. Coincident-reciprocal simply refers to QTLs where alleles associated with increased severity of the withdrawal response are also associated with decreased ethanol consumption and vice-versa. However, this renewal application also moves in a new direction. The rationale for this new direction builds from the idea that the natural genetic variation in any complex trait phenotype is a systems biology phenomenon and must be investigated as such.Systems can be defined in many different ways. Here systems are generally defined as gene co-expression networks (Langfelder and Horvath, 2008) strongly associated with the consumption and withdrawal phenotypes. These networks are detected independently of the QTL analysis. Importantly, network structure provides both a new tool for interrogating QTL intervals and the broader context for determining how a single gene can affect the phenotypes of interest. The systems biology approach also emphasizes that it is the system and not any individual gene that has translational value. This competing renewal has 4 specific aims. 1) To fine map in heterogeneous stock (HS4) animals (to a resolution of 1 cM or less) 7 high quality QTLs for consumption and acute withdrawal. Mapping in HS4 will provide QTL resolution of approximately 1-2 cM. 2) To integrate the QTL data with interval relevant gene expression and sequence data. As each interval will be completely sequenced, all causative polymorphisms will be detected. In addition to standard oligonucleotide gene expression analysis, we will test the idea that some QTLs may be generated by alternative exon usage. 3) To determine in the STSB lines the gene networks associated with consumption and acute withdrawal. The clusters (modules) of genes coincidently associated with consumption and withdrawal will be interrogated to determine if there is overlap with the candidate quantitative trait genes generated from aims 1 and 2. 4) To determine if the consumption and withdrawal modules identified in aim 3 are also detected in a more genetically diverse mouse population. Aim 4 will employ short-term selective breeding from an outbred population of the collaborative cross mouse. To our knowledge, this is the first test for a behavioral trait of the idea that network or module trumps genetic diversity.